1 Introduction
Graph games are twoplayer zerosum games with deep connections to foundations of logic [25] as well as numerous practical applications, e.g., verification [15], reactive synthesis [23], and reasoning about multiagent systems [2]. The game proceeds by placing a token on one of the vertices and allowing the players to move it throughout the graph to produce an infinite trace, which determines the winner or payoff of the game. Traditionally, the players alternate turns when moving the token. In bidding games [19, 18], however, the players have budgets, and in each turn, we hold an “auction” (bidding) to determine which player moves the token.
Four concrete bidding mechanisms have been previously defined. In all mechanisms, in each turn, both players simultaneously submit a bid that does not exceed their available budget, and the higher bidder moves the token. The mechanisms differ in their payment schemes, which we classify according to two orthogonal properties. First, in
firstprice bidding only the higher bidder pays his bid, whereas in allpay bidding, both players pay their bids. Second, in Richman bidding (named after David Richman) the payment(s) are paid to the other player whereas in poorman bidding the payments are paid to the “bank”, thus the money is lost. We refer to the mechanisms using abbreviations in . For example, FPpoor refers to firstprice poorman and APRich refers to allpay Richman. The central quantity in bidding games is the budget ratio; for , when Player ’s budget is , then his budget ratio is .Reachability FPRich and FPpoor games were studied in [19, 18]; each player has a target vertex, and the game ends once one of the targets is reached. It was shown that each vertex of the game has a threshold ratio, which is a necessary and sufficient initial budget for winning the game. Moreover, if a player has a sufficient budget for winning, he has a deterministic winning strategy. Only under FPRich bidding do reachability games have an intriguing equivalence with a class of games called randomturn games [22], in which in each turn, we toss a coin to determine which player moves the token (see more details in [18, 4]).
Reachability APpoor games were only recently studied [7]. Technically, these games are significantly harder than firstprice bidding: e.g., optimal strategies randomize over biddings, and already in very simple games, infinite support is required. Moreover, fundamental questions about this model are open.
Infiniteduration bidding games were studied under FPRich [4] and FPpoor [5] bidding. For qualitative objectives, e.g., parity, bidding games reduce to reachability firstprice bidding games by showing that one of the players wins a stronglyconnected game with any positive initial ratio. Things get more interesting with meanpayoff objectives, which are quantitative (see Fig. 2 for an example): an infinite play has a payoff which is Player ’s reward and Player ’s cost, thus in these games the players are called Max and Min, respectively. The central question is identifying the optimal payoff Max can guarantee in a meanpayoff game with an initial budget ratio .^{1}^{1}1Technically, is optimal when Max can guarantee a payoff that is greater than , for every .
Meanpayoff firstprice bidding exhibit intriguing equivalences with randomturn games. For a bias , we denote by
, the randomturn game in which in each turn, we toss a coin that selects Max to move with probability
and Min with probability . The game is a meanpayoff stochastic game [13], its meanpayoff value, denoted , is a wellknown concept [24]. Under FPRich bidding, the initial budget ratio does not matter, and the optimal payoff Max can guarantee is . Thus, similar to reachability FPRich games, meanpayoff FPRich games are equivalent to fair randomturn games. Since such an equivalence is not known for reachability FPpoor games, it is surprising that meanpayoff FPpoor games are equivalent to randomturn games and the equivalence is in fact richer than for FPRich bidding: with an initial ratio of , the optimal payoff Max can guarantee under FPpoor bidding is . Thus, the initial ratio matters and coincides with the bias of the coin in the randomturn game. For example, in the game depicted in Fig. 2, with a ratio of , Max prefers FPpoor over FPRich, since with the first he can guarantee a payoff of and with the second, only .We study, for the first time, infiniteduration allpay bidding games. The starting point of this research is inspired by the results for FPpoor bidding: the moral of these results is that as we “go to the infinity”, bidding games become cleaner and exhibit a more elegant mathematical structure. We ask: Does this phenomenon also hold for allpay bidding, where reachability games are highly complex? Would infiniteduration allpay bidding games reveal a clean mathematical structure like their firstprice counterparts? We answer both of these questions positively.
Before surveying our results, we note that in terms of applications, allpay bidding is often better suited for modelling practical applications than firstprice bidding. Applications arise from viewing the players’ budgets as resources with little or no inherent value, e.g., time or strength, and a strategy as a recipe to invest resources with the goal of maximizing the expected utility. In many settings, invested resources are lost, thus allpay bidding is more appropriate than firstprice bidding. Reachability APpoor games [7] can be seen as a dynamic variant of Colonel Blotto games [9], which have been extensively studied. Applications of Colonel Blotto games, which carry over to allpay bidding games, include political lobbying and campaigning, rent seeking [27], and modelling biological processes [12]. In fact, due to their dynamic nature, bidding games are a better model for these applications.
Another application of bidding games is reasoning about systems in which the scheduler accepts payment in exchange for priority. Blockchain technology is one such example. Simplifying the technology, a blockchain is a log of transactions issued by clients and maintained by miners, who accept transaction fees from clients in exchange for writing transactions to the blockchain. In Etherium, the blockchain consists of snippets of code (called smart contracts). Verification of Etherium programs is both challenging and important since bugs can cause loss of money (e.g., [11]). Bidding games, and specifically APpoor games, can model Etherium programs: we associate players with clients and, as is standard in model checking, we associate the states of the program with the vertices of the graph. APpoor bidding is the most appropriate bidding mechanism since in Etherium, the transaction fees are always paid to the miners.
We start by studying allpay Richman biding and showing a complete picture for this bidding mechanism. We focus on games played on stronglyconnected graphs. A simple argument shows that a deterministic Max strategy cannot guarantee anything under APRich bidding. We turn to study probabilistic strategies, which is technically more challenging. We show that meanpayoff APRich games are essentially equivalent to their firstprice counterparts: in a game , with any positive initial ratio, Max’s optimal probabilistic strategy guarantees an expected payoff that equals the value of under FPRich, which in turn equals ; namely, the value of the fair randomturn game.
In parity APRich games, we show that one of the players has a randomized strategy that guarantees winning with probability , and the winner depends only on the highest parity index in the game. Showing, again, an equivalence between parity APRich and FPRich games. Our almostsure winning strategy in a parity APRich game is obtained by evoking carefully and repeatedly an optimal strategy in a meanpayoff APRich game so that even though the guarantee in is on the expected payoff, we obtain almostsure guarantees in .
Our solution of meanpayoff APRich games is based on a new significantlysimpler construction of optimal strategies in meanpayoff FPRich games. The previous constructions of optimal FPRich strategies [4, 6] use the history of the play to determine the next bid; namely, the bid is roughly a function of the difference between the number of Max wins and loses during the play. It is technically not possible to use such a strategy in APRich, as we illustrate in the example below. We devise new FPRich strategies that are history independent: the bid depends only the current budget and vertex, hence the name budgetbased strategies, which are interesting in their own right.
We describe a simple Max strategy for the meanpayoff game that is depicted in Fig. 2, which achieves an expected payoff of under APRich bidding; still not optimal, but better than any deterministic strategy can achieve. We start with the following observation. Suppose Max chooses a bid uniformly random from , for some . We assume Min wins ties. Thus, knowing Max’s strategy, Min chooses between deterministically bidding or . There are four possible outcomes (see Table 2). The “bad” outcomes for Max are and since Min wins without any budget penalty. The two other outcomes are “good” since they are similar to FPRich bidding: Max pays for winning and gains when losing. To obtain a probabilistic strategy for APRich, we use an optimal strategy for FPRich as in [4], which achieves a payoff of by guaranteeing that Max wins as many biddings as Min (up to a constant). We input to the difference between Max wins and losses when restricting to good outcomes to obtain . Intuitively, we expect half the outcomes in a play to be good, out of these, guarantees that Max roughly wins half the biddings, for a total payoff of .
The construction above relies on a classification of outcomes to “good” and “bad”. To obtain an optimal strategy for APRich, we would like decrease to a minimum the probability mass of the “bad” outcomes by bidding uniformly at random in . But then, it is not clear how to use to obtain . For example, when Min bids , both when Max bids and , he wins the bidding. However, Max is “luckier” in the first outcome since he pays less for winning. To overcome this issue, we devise a new optimal budgetbased bidding strategy for FPRich. Intuitively, the current budget reflects precisely how “lucky” Max was in the previous biddings. We construct an optimal strategy under APRich that bids uniformly at random from . ∎
Finally, we study meanpayoff allpay poorman bidding. Poorman bidding games tend to be unpredictable and technically more challenging than Richman bidding. APpoor is no exception. We show that, contrary to APRich, deterministic strategies are useful under APpoor bidding. Consider a game and suppose Max’s ratio is . We show that the optimal payoff Max can guarantee with a deterministic strategy in is . Not too far from the optimal payoff under FPpoor, which is . The result immediately implies that in a parity APpoor game, one of the players wins with a ratio greater than . Here too, we first revisit meanpayoff FPpoor games and construct optimal budgetbased strategies, which are significantly simpler than previous constructions. We leave open the problem of finding optimal probabilistic strategies for APpoor bidding.
Further related work: All the results surveyed above highly depend on the fact that the players’ bids can be arbitrarily small. This is a problematic assumption for practical applications. To address this limitation discrete bidding games were studied in [14], where the budgets are given in “cents” and the minimal positive bid is one cent. Their motivation came from recreational play like bidding chess [8, 17]. APpoor is not suited for discrete bidding since the budgets run out quickly. Discrete APRich bidding has been studied in [21] (we encourage the reader to try playing APRich tictactoe online: http://tiny.cc/hqbgoz). While the issue of tie breaking does not play a key role in continuous bidding, it is important in discrete bidding [1]. Nonzerosum FPRich games were studied in [20].
2 Preliminaries
A bidding game is a twoplayer game that is played on a directed graph , where is a finite set of vertices and is a set of directed edges. The neighbors of a vertex is the set of vertices . A path in is a finite or infinite sequence of vertices such that for every , we have . We denote by , the set of simple cycles in . We call a bidding game stronglyconnected when the graph is stronglyconnected. For , we use as short for when referring to the “other player”.
2.1 Bidding games, strategies, and plays.
The game proceeds as follows. We place a token on one of the vertices in the graph. In each turn, we hold a bidding to determine which player moves the token. Formally, a strategy is a recipe for how to play a game. It is a function that, given a finite history
of the game, prescribes to a player a probability distribution over
actions to take, where we define these two notions below. A strategy is deterministic when it always prescribes one action with probability .Actions in bidding games are pairs of the form , where is a bid and is the vertex to move the token to upon winning the bidding. Suppose Player , for , bids in a bidding. The winner of the bidding is Player if and otherwise it is Player . That is, we resolve ties in favor of Player . This is an arbitrary decision and our results are not affected by the tiebreaking mechanism that is used. A history in a bidding game is , where for , the token is placed on vertex at round , and , denotes Player ’s bid at round , for . An action is legal following if (1) is a neighbor of , thus a history gives rise to a path in the graph, which we denote by , and (2) the bid does not exceed the available budget as we define below.
For , we denote by the indices in which Player wins and loses biddings, respectively. Suppose Player ’s initial budget is . The available budget of Player following , denoted , depends on the bidding mechanism, and we consider four bidding mechanisms that differ in the payment scheme in each bidding:

Firstprice Richman: only the winner pays the loser, thus .

Firstprice poorman: only the winner pays the “bank”, thus .

Allpay Richman: both players pay each other, thus .

Allpay poorman: both players pay the “bank”, thus .
(Budgetbased strategy). A strategy is called budgetbased if it is independent of the history of the play and it is a function from the current vertex and budget to a vertex and a probability distribution on bids.
An initial vertex and two strategies and for the players give rise to a probability distribution over infinite plays, which we denote by , where we omit the initial vertex since it usually does not play a role in our results. When and are deterministic, there is a unique play that gets probability in , and we denote it by . It is defined inductively as follows. The initial vertex is . Let be a finite play that ends in , we define its continuation as follows. Let and . Then, if , we define and otherwise . Since we consider probabilistic strategies with continuous support, the definition requires us to define a probability space using a cylinder construction [3, Theorem 2.7.2], which is technical but standard and we do not present it here. When and are clear from the context, we omit them and simply write and instead of and expectation .
2.2 Objectives and values
The central quantity in bidding games is the ratio between the players’ budgets. (Budget ratio). For , let be Player ’s budget. Then, Player ’s ratio is . In Richman bidding, since the money only exchanges hands, the sum of budgets is constant and we normalize it to . In poorman bidding, we often normalize Player ’s budget to .
A qualitative objective is a set of infinite paths. The central question in qualitative bidding games concerns the necessary and sufficient initial ratio for guaranteeing that an objective is satisfied. The qualitative objectives that we consider are:

Reachability: Player has a target vertex and an infinite play is winning iff it visits .

Parity: Each vertex is labeled with an index in . An infinite path is winning for Player
iff the parity of the maximal index that is visited is odd.
(Winning strategies). Consider a Player strategy . We say that surely wins if it is deterministic and for every deterministic strategy , we have . We say that almostsurely wins if for every strategy , we have .
The quantitative objective we focus on is meanpayoff. Each play in a meanpayoff game has a payoff, which is Player ’s reward and Player ’s cost, thus in meanpayoff games, we refer to Player as Max and to Player as Min. The central question in meanpayoff bidding games concerns the optimal payoff a player can guarantee with an initial ratio, called the meanpayoff value. Technically, a meanpayoff game is played on a weighted directed graph , where . Consider an infinite path . For , the prefix of length of is . The energy of is . We define the payoff of an infinite and finite paths. For an infinite path and , we define and . Note that the definition gives Min an advantage. For , let and respectively denote the set of deterministic and probabilistic strategies for Player with an initial ratio of .
(Meanpayoff value). Consider a meanpayoff game and a ratio .

The expectedvalue of w.r.t. , denoted , is if for every and no matter where the game starts, Max has a probabilistic strategy s.t. for every , we have , and dually, there is s.t. for every , we have .

The surevalue of w.r.t. , denoted , is if Max can deterministically guarantee a payoff of : for every and no matter where the game starts, there is s.t. for every , we have . And, Max cannot do better: for every Max strategy , there is that guarantees .
We sometimes write , for to highlight the bidding mechanism that is used. We note that in order to show that guarantees an expected payoff of , it suffices to show that it guarantees an expected payoff of against every deterministic strategy .
2.3 Randomturn games, strengths, and potentials
In this section, we describe the tool that, intuitively, allows us to extend a solution for the game (Fig. 2) to a general stronglyconnected game. In such games, we need a measure of how “important” it is to choose the successor of a vertex, which we call the strength. The higher the strength of , the higher a player should bid while the token is in . To define strengths, we need several definitions.
A randomturn game [22] parameterized by is played on a graph as a bidding game, only that instead of bidding for moving, we throw a (biased) coin to determine which player moves the token: Player is chosen with probability and Player with probability . Formally, a randomturn game is a stochastic game [13]. The objective in matches that of , and we focus on stronglyconnected meanpayoff randomturn games. The meanpayoff value of , denote , is defined as the expected payoff that a player can guarantee. It is wellknown that it is achievable using positional strategies [24]. Such a strategy takes as input the vertex the token is placed on, and returns which vertex to move the token to upon being selected by the coin toss. Let and respectively denote optimal positional strategies for Max and Min. For , we denote and . The concept of potentials was originally defined in the context of the strategy iteration algorithm [16]. We denote the potential of a vertex by and the strength of by , and we define them as solutions to the following equations.
There are optimal strategies for which , for every neighbor of , which can be found, for example, using the strategy iteration algorithm. Note that , for every . We denote the maximal strength by and we assume otherwise the game is trivial as all weights are equal.
Consider a finite path in . We intuitively think of as a play, where for every , the bid of Max in is and he moves to upon winning. Thus, when , we think of Max as investing and when , we think of Min winning the bid thus Max gains . We denote by and the sum of investments and gains, respectively. Note that and are defined w.r.t and will be clear from the context. Recall that the energy of is . The following lemma connects the energy, potentials, and strengths.
[6] Consider a stronglyconnected game , and . For every finite path in , we have . In particular, when for , there is a constant such that .
The proof of the following corollary can be found in App. A.
Consider a stronglyconnected game , let be an infinite play, let , and . If for every finite prefix of , then .
3 AllPay Richman Bidding Games
In this section we completely solve infiniteduration allpay Richman bidding games. We focus on stronglyconnected games. We start with the following negative result that shows that deterministic strategies are useless in allpay Richman bidding.
Let be a stronglyconnected game. For any initial ratio and a deterministic Player strategy, Player has a strategy that wins all but a constant number of biddings. Specifically,

When is a meanpayoff game, the surevalue of is the lowest possible; namely, ;

When is a parity game that has a cycle in with an even maximal parity index, then no deterministic Player strategy can guarantee winning with a positive probability.
Proof.
Let be Player ’s initial budget. Consider a deterministic Player strategy that, for , bids in the th bidding. Player bids when , and other he bids . Suppose Player ’s initial budget is . Since whenever Player loses a bidding, Player bids at least , and the sum of Player ’s payments to Player in a finite play is less than , Player can win at most times. ∎
3.1 Revisiting meanpayoff firstprice Richman games
Our construction of optimal probabilistic strategies in meanpayoff APRich games is based on a new significantlysimpler construction of budgetbased strategies for meanpayoff FPRich games. Our constructions throughout the paper use the following definition: (Shift function). The shift function is defined as . The proof of the following lemma can be found in App. C. The shift function has the following properties:

For every , there exists such that .

For every , we have .
Previous constructions of optimal strategies for meanpayoff FPRich games can be found in [4, 6]. Below we revisit meanpayoff FPRich games and devise optimal budgetbased strategies. For ease of presentation, we illustrate the construction on the simple game , and it can easily be extended to general SCCs, as we note in the proof.
Consider the game , depicted in Fig. 2. Under firstprice Richman bidding, for every initial ratio and , Max has a deterministic budgetbased strategy that guarantees a payoff of at least , thus .
Proof.
Let be Max’s initial budget. It is convenient to set the weights in the game to and , where depends on , and setting Max’s goal to keep the energy bounded from below by a constant. Then, as the length of the play tends to infinity, the ratio of Max’s wins tends to , thus the payoff in the original game is . Let such that (see Lem. 3.1). Max maintains the invariant that when the energy is , his budget exceeds . We choose an initial such that the invariant holds for , which is possible since . Max keeps the energy above .
Max plays as follows. When Max’s budget is , he bids . Note that the strategy is budget based since the bid depends only on the current budget.^{2}^{2}2For general SCCs, at a vertex , we bid , see more details in Lem. 3.2. To conclude the proof, we show that the invariant is maintained and that it guarantees that . We distinguish between the two outcomes of a bidding. If Max loses, the energy decreases to . Moreover, Min overbids Max, thus Max’s new budget is at least . On the other hand, if Max wins, the energy increases to and his new budget is at least . By plugging in and in Lemma 3.1, we obtain , thus , as required. To conclude the proof, we have , since otherwise, by the invariant, Max’s budget would be , which is impossible since the budgets sum up to in Richman bidding. ∎
3.2 Meanpayoff allpay Richman games with probabilistic strategies
This section consists of our main technical contribution on allpay Richman bidding. We find optimal probabilistic strategies and show that they do not depend on the initial ratio, thereby showing equivalence between meanpayoff allpay and firstprice Richman games.
Let be a stronglyconnected meanpayoff allpay Richman bidding game. For every initial ratio of Max, for every , Max has a probabilistic budgetbased strategy in that guarantees an expected payoff of at least .
Proof.
Let , and let . We find vertex strengths using as in Section 2.3. Let s.t. (see Lemma 3.1). We define Max’s strategy as follows.

When the token is on vertex with strength , and Max’s budget is , Max bids .

Upon winning, Max moves the token to .
We claim that no matter which deterministic strategy Min chooses, we have . We assume Wlog that Min bids , since he has the tiebreaking advantage and does not profit from bidding higher.
Proofs in firstprice bidding show an invariant between changes in energy and changes in budget (see Prop. 3.1). For allpay bidding, however, such an invariant is not possible. Our invariant uses a new component, which we refer to as “luck”. Intuitively, a Max bid of is “unlucky” when is much larger than or when is slightly smaller than . In the first case, Max pays a lot for winning and in the second, Min pays little for winning. The topleft to bottomright diagonal in Tab. 2 take these scenarios to the extreme. Dually, when is just above or way below , Max is “lucky”. See the other diagonal in the table. The key idea of the proof is that on average, the unlucky and lucky cases cancel out.
To formalize the notion of luck, we define a quantity as follows. Initially, we set . We define the change in luck following a bidding
(3.1) 
We formalize the intuition that lucky and unlucky events cancel out with the following claim, proved in App. D.
Claim: Suppose that following a finite play, Max bids , then for every Min bid , we have
(3.2) 
For any finite play , recall that and are the sum of the strengths of the vertices where Max wins, respectively loses. We prove in App. E that the budget of Max after the play satisfies the following invariant, by induction on the length of the play and using Bernoulli’s inequality.
Claim: For every finite play coherent with the strategy of Max, we have
(3.3) 
Since the sum of budgets is , we have . Comparing the exponents, we obtain . Since at each turn and for each , we conclude that for any deterministic strategy of Min we have where the expectation is taken over the probability space of all plays defined by and . Combining with Lemma 2.3, by plugging and , we finally obtain
(3.4) 
Dividing by , we obtain , and we are done. ∎
Since is continuous in [10, 26], it follows from Lemma 3.2 that , for every . Note that since the definition of payoff favors Min, Min can follow Max’s strategy above to show that Max cannot achieve a payoff of . We thus have the following.
Let be a stronglyconnected meanpayoff allpay Richman bidding game. The expected meanpayoff value in equals the surely meanpayoff value in under firstprice bidding, which in turn equals the meanpayoff value of the randomturn game in which the player who moves is chosen uniformly at random, thus for every budget ratio , we have .
3.3 Qualitative allpay Richman games
In this section we consider stronglyconnected parity APRich games. Recall that under FPRich, one of the players wins with any initial budget, and the winner depends on the parity of the highest parity index. Analogously, we show that in parity APRich games, one of the players wins with probability with any positive initial budget.
Let be a parity game in which the maximal parity index is . We construct a meanpayoff game by setting the weight of a vertex to be if the parity of is , and otherwise . The key property of this weight function is that any path with must visit a vertex with index infinitely many times, and thus satisfies the parity objective. The proof of the following lemma can be found in App. F.
Let be a stronglyconnected meanpayoff game with nonnegative weights and at least one strictly positive weight. Then, for every , we have .
The challenge in using an optimal strategy for in is that the guarantees in are on the expected payoff, and they do not immediately imply almostsure guarantees in . We manage to recover these guarantees by carefully and repeatedly invoking to obtain the following result.
Let be a stronglyconnected parity allpay Richman bidding game in which the highest parity is . Then, the player corresponding to the parity of has a randomized almostsure winning strategy, no matter where the game starts and with which positive initial ratio.
Proof.
We assume WLog that the highest parity index is odd and denote by , the set of vertices with parity index . We construct as in the above. Since is stronglyconnected, for each by Thm. 3.2, we have . Moreover, the proof of Lemma 3.2 shows that there exists a strategy which guarantees a payoff of at least independent of the initial vertex and the initial budget. By Lemma 3.3, we have for some .
Next, we define a function that takes an initial ratio , and returns an index such that for every , for every strategy of the opponent we have . It follows from eq. (3.4), that , where is a constant depending on . We define hence, as required, for every we have
The proof of the following claim can be found in App. G.
Claim: If Player follows the strategy with an initial budget , then the probability of visiting a vertex in during the first steps is at least .
We are now ready to define the Player strategy that almostsurely guarantees the parity objective. Let be the initial budget of Player . We partition the budget into portions , and the time over which the game evolves into time periods , where each is of length . Then for each , the strategy uses portion of the initial budget to follow during the time period .
We claim that guarantees almostsurely visiting infinitely often. We prove this by contradiction and suppose that there exists a strategy for the opponent such that is visited only finitely many times with positive probability (over the probability space defined by and ). This implies that
where the last inequality follows by the union bound. Hence, there exists such that . This event is equivalent to not being visited in time periods , which in turn implies that for every the probability of not being visited in between time and is also positive. But for a fixed this probability can be factorized as
(3.5) 
A key point in the proof of the following claim is that the construction of is budgetbased. As the budget used by the strategy at the beginning of time period is , by expanding each of the factors in eq. 3.5 as a sum over the initial vertex of the time period, one can deduce the claim.
Claim: For each we have

, and

. ∎
4 AllPay Poorman Bidding Games
In this section, we construct optimal deterministic strategies in allpay poorman games. We find it surprising that, contrary to allpay Richman bidding games (Theorem 3), deterministic strategies are useful in allpay poorman bidding games. Throughout this section it is technically convenient to keep Min’s budget normalized to , thus for a budget of Max, we consider the ratio .
4.1 Revisiting meanpayoff firstprice poorman games
The value of meanpayoff firstprice poorman games was first identified in [5].
[[5]] Let be a stronglyconnected meanpayoff allpay poorman bidding game. For every initial ratio of Max, we have .
We revisit this result and provide an alternative proof by constructing new and significantly simpler optimal budgetbased bidding strategies. We then base our solution to allpay bidding on the budgetbased strategy.
Let be a stronglyconnected meanpayoff allpay poorman bidding game. For every initial ratio of Max, for every , Max has a deterministic budgetbased strategy that guarantees a payoff of .
Proof.
Let be Max’s initial budget. As usual, we keep Min’s budget normalized to . Let , and let . We design a deterministic strategy for Max that maintains his budget above . Then, the value of the updated budget of Max after a bidding where Max bids and Min bids is as follows: If Max wins the bidding (), we have . The key new insight is that when Max loses the bidding (), we have
(4.1) 
Intuitively, the property states that every cent is times more valuable to Min than it is to Max. For example, if Max’s budget is and Min’s budget is , then paying is twice as painful for Min as it is for Max. Roughly, on average, this means that Max wins times more biddings than Min, thus he guarantees a payoff close to .
We now proceed to define formally a budgetbased bidding strategy for Max that guarantees a payoff of at least , where . We pick satisfying (see Lemma 3.1). We find vertex strengths using as in Section 2.3. Let . The strategy is defined as follows.

When the token is placed on a vertex with strength and Max’s budget is , Max bids .

Upon winning, Max moves the token to .
We first show that Max’s bidding strategy is legal, by showing that we always have . Indeed, initially, we have , and then whenever Max loses a bidding his budget increases, and when Max wins a bidding his updated budget is , which is still greater than since .
Next, for any finite play , let . Recall that and denote the sum of the strengths of the vertices of where Max wins, respectively loses. We prove in App. H that the budget of Max after the play satisfies the following invariant, using induction on the length of and Bernoulli’s inequality.
Claim: For every finite play coherent with the strategy of Max, we have
(4.2) 
As a consequence, we show the existence of a bound such that for every finite play coherent with .
Claim: There exists such that for every finite play coherent with , we have
(4.3) 
The proof of the claim, found in App I, can be summarised as follows. Since the lefthand side of the equation is equal to , proving the claim is equivalent to proving a lower bound for . To do so, we show that cannot get too low, as past some threshold Equation (4.2) guarantees that the budget of Max is so high that his next bid according to the strategy will be above (the whole budget of Min). Thus Max is guaranteed to win the next bidding, which results in going back up.
4.2 Infiniteduration allpay poormanbidding games
This section consists of our main technical contribution for allpay poorman bidding. We first solve meanpayoff games, and then use our solution to solve parity games. Recall that, the value of a stronglyconnected meanpayoff game w.r.t. an initial ratio under FPpoor is . The following theorem shows that the surevalue under allpay poorman bidding is not far from the surevalue under firstprice poorman bidding.
Let be a stronglyconnected meanpayoff allpay poorman bidding game. For every initial ratio of Max, we have .
Let be a stronglyconnected meanpayoff allpay poorman bidding game. For every initial ratio of Max, for every ,

Max can deterministically guarantee a payoff of ;

For any fixed deterministic strategy of Max, Min can guarantee a payoff of .
Proof.
In App. J, we show that the strategy that we constructed in Prop. 4.1 for FPpoor guarantees a payoff of under APpoor. The analysis needs to be adjusted to APpoor. The main difference is that the change in Max’s budget following losing a bidding to a bid of Min is now
For Item ii, consider a deterministic strategy of Max. We describe a Min strategy as follows. Let , let , and we choose satisfying . For each vertex with strength and a Max budget , Min fixes a threshold . When the token is placed on , Min computes the bid that Max will bid according to . If , Min bids , wins the bidding and moves to . If , Min judges that the budget required to win the bidding is not worth it, and stays out by bidding . In App. K, we show that guarantees a payoff of . Intuitively, technically, the threshold is where the invariant on Max’s budget is (close to) an equality no matter the bid of Min. Thus, if Max bids higher than , he is paying “too much” for a win and eventually his budget will run out. ∎
In App. L, we show that a construction of a meanpayoff game from a parity game as in Sec. 3.3 together with Lem. 4.2 provides the following.
Consider a stronglyconnected parity game in which the highest priority is odd. For every initial ratio , Player has a surely winning strategy in .
5 Discussion
We study, for the first time, infiniteduration allpay bidding games. We show a complete picture for allpay Richman bidding: deterministic strategies cannot guarantee anything, and with probabilistic strategies, APRich coincides with FPRich both for meanpayoff and parity objectives. For allpay poorman bidding, we show that, surprisingly, deterministic strategies can guarantee a payoff not too far from the optimal payoff under FPpoor bidding.
We leave open the problem of classifying the expected value in a meanpayoff APpoor bidding game . It is tempting to conjecture that as in APRich bidding, the expected value coincides with the surevalue under firstprice bidding.However, this might not be true due to the following difference between the bidding rules. When both players bid (see the bottomright cell in Table 2), the situation under APRich bidding is worse than under APpoor bidding. Indeed, with APRich, Max’s updated budget is , whereas with APpoor, his budget is , and we have when . This slight difference might lead to .
Taxman bidding spans the spectrum between Richman and poorman bidding: for a constant , portion is paid to the other player player and portion is paid to the bank. Meanpayoff firstprice taxman bidding games were studied in [6] with the motivation to better understand the differences between FPRich and FPpoor bidding. The same motivation applies to allpay bidding, and we find it interesting to study allpay taxman bidding. Unlike firstprice bidding, with allpay bidding classifying both the sure and expectedvalue for allpay taxman bidding are interesting problems.
In terms of computational complexity, since solving stochastic games is in NP and coNP, the equivalences between stronglyconnected meanpayoff bidding games and randomturn games implies the same upper bound on the problem of finding the sure or expected value of the corresponding bidding game. Finding a lower bound, e.g., that bidding games are harder than general stochastic games, is an open problem also for firstprice bidding.
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Appendix A Proof of Corollary 2.3
Given an infinite play and , let us denote by the prefix of of length . Suppose that for every . Then applying Lemma 2.3 yields
This concludes the proof, as both fractions tend towards as approaches .
Appendix B Bernoulli’s inequality
Bernoulli inequality is stated as follows.
(B.1) 
Appendix C Properties of the shift function
The shift function is surjective as

(l’Hôpital rule);

;

is continuous as its denominator is strictly positive over the domain, and is continuous.
As a consequence, for every , there exists such that . The second item is a direct consequence of the definition of the shift function.
Appendix D Proof of Equation 3.2
Let denote
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